Overview

Dataset statistics

Number of variables20
Number of observations513863
Missing cells240184
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.4 MiB
Average record size in memory160.0 B

Variable types

Numeric12
Categorical8

Alerts

date_time has a high cardinality: 43182 distinct valuesHigh cardinality
zipcode has a high cardinality: 15038 distinct valuesHigh cardinality
year_home_built is highly overall correlated with days_since_last_visitHigh correlation
home_market_value is highly overall correlated with net_worth and 5 other fieldsHigh correlation
net_worth is highly overall correlated with home_market_value and 2 other fieldsHigh correlation
income is highly overall correlated with home_market_value and 2 other fieldsHigh correlation
mkt_organic_product_purchasers_value is highly overall correlated with home_market_value and 3 other fieldsHigh correlation
mkt_trend_env_focused_hh_value is highly overall correlated with home_market_value and 4 other fieldsHigh correlation
high_end_shoppers_value is highly overall correlated with home_market_value and 4 other fieldsHigh correlation
do_it_yourselfer_value is highly overall correlated with home_market_value and 3 other fieldsHigh correlation
mkt_green_product_purchasers_value is highly overall correlated with mkt_organic_product_purchasers_value and 1 other fieldsHigh correlation
days_since_last_visit is highly overall correlated with year_home_builtHigh correlation
success is highly imbalanced (71.5%)Imbalance
days_since_last_visit has 107835 (21.0%) missing valuesMissing
year_home_built has 11962 (2.3%) missing valuesMissing
home_market_value has 11962 (2.3%) missing valuesMissing
length_of_residence has 11962 (2.3%) missing valuesMissing
net_worth has 11962 (2.3%) missing valuesMissing
income has 11962 (2.3%) missing valuesMissing
mkt_organic_product_purchasers_value has 11962 (2.3%) missing valuesMissing
mkt_trend_env_focused_hh_value has 11962 (2.3%) missing valuesMissing
high_end_shoppers_value has 11962 (2.3%) missing valuesMissing
do_it_yourselfer_value has 11962 (2.3%) missing valuesMissing
montrd_home_security_sys_own_value has 11962 (2.3%) missing valuesMissing
mkt_green_product_purchasers_value has 11962 (2.3%) missing valuesMissing
visit_id is uniformly distributedUniform
visit_id has unique valuesUnique

Reproduction

Analysis started2022-12-29 18:29:16.164758
Analysis finished2022-12-29 18:31:24.847699
Duration2 minutes and 8.68 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

visit_id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct513863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256931
Minimum0
Maximum513862
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:25.145613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25693.1
Q1128465.5
median256931
Q3385396.5
95-th percentile488168.9
Maximum513862
Range513862
Interquartile range (IQR)256931

Descriptive statistics

Standard deviation148339.62
Coefficient of variation (CV)0.57735195
Kurtosis-1.2
Mean256931
Median Absolute Deviation (MAD)128466
Skewness1.9078039 × 10-19
Sum1.3202733 × 1011
Variance2.2004641 × 1010
MonotonicityNot monotonic
2022-12-29T13:31:25.579576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14167 1
 
< 0.1%
191702 1
 
< 0.1%
331337 1
 
< 0.1%
148651 1
 
< 0.1%
458868 1
 
< 0.1%
469465 1
 
< 0.1%
504982 1
 
< 0.1%
450156 1
 
< 0.1%
425742 1
 
< 0.1%
407741 1
 
< 0.1%
Other values (513853) 513853
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
513862 1
< 0.1%
513861 1
< 0.1%
513860 1
< 0.1%
513859 1
< 0.1%
513858 1
< 0.1%
513857 1
< 0.1%
513856 1
< 0.1%
513855 1
< 0.1%
513854 1
< 0.1%
513853 1
< 0.1%

date_time
Categorical

Distinct43182
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
6/24/19 13:23
 
51
6/27/19 12:41
 
47
6/27/19 9:29
 
47
6/28/19 11:20
 
47
6/27/19 13:53
 
47
Other values (43177)
513624 

Length

Max length13
Median length13
Mean length12.557596
Min length11

Characters and Unicode

Total characters6452884
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1710 ?
Unique (%)0.3%

Sample

1st row5/31/19 10:36
2nd row5/31/19 10:37
3rd row5/31/19 10:37
4th row5/31/19 10:37
5th row5/31/19 10:37

Common Values

ValueCountFrequency (%)
6/24/19 13:23 51
 
< 0.1%
6/27/19 12:41 47
 
< 0.1%
6/27/19 9:29 47
 
< 0.1%
6/28/19 11:20 47
 
< 0.1%
6/27/19 13:53 47
 
< 0.1%
6/27/19 13:50 46
 
< 0.1%
6/27/19 13:47 46
 
< 0.1%
6/28/19 12:48 46
 
< 0.1%
6/24/19 13:14 46
 
< 0.1%
6/26/19 13:46 45
 
< 0.1%
Other values (43172) 513395
99.9%

Length

2022-12-29T13:31:26.063284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6/27/19 26184
 
2.5%
6/24/19 25534
 
2.5%
6/26/19 25338
 
2.5%
6/25/19 24464
 
2.4%
6/28/19 23988
 
2.3%
6/20/19 21734
 
2.1%
6/18/19 21638
 
2.1%
6/19/19 21011
 
2.0%
6/21/19 20591
 
2.0%
6/30/19 20468
 
2.0%
Other values (1461) 796776
77.5%

Most occurring characters

ValueCountFrequency (%)
1 1240089
19.2%
/ 1027726
15.9%
9 672449
10.4%
6 645589
10.0%
2 547019
8.5%
513863
8.0%
: 513863
8.0%
3 265145
 
4.1%
0 263272
 
4.1%
5 229643
 
3.6%
Other values (3) 534226
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4397432
68.1%
Other Punctuation 1541589
 
23.9%
Space Separator 513863
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1240089
28.2%
9 672449
15.3%
6 645589
14.7%
2 547019
12.4%
3 265145
 
6.0%
0 263272
 
6.0%
5 229643
 
5.2%
4 228887
 
5.2%
8 156344
 
3.6%
7 148995
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 1027726
66.7%
: 513863
33.3%
Space Separator
ValueCountFrequency (%)
513863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6452884
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1240089
19.2%
/ 1027726
15.9%
9 672449
10.4%
6 645589
10.0%
2 547019
8.5%
513863
8.0%
: 513863
8.0%
3 265145
 
4.1%
0 263272
 
4.1%
5 229643
 
3.6%
Other values (3) 534226
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6452884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1240089
19.2%
/ 1027726
15.9%
9 672449
10.4%
6 645589
10.0%
2 547019
8.5%
513863
8.0%
: 513863
8.0%
3 265145
 
4.1%
0 263272
 
4.1%
5 229643
 
3.6%
Other values (3) 534226
8.3%

experience
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
version2
257284 
version1
256579 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4110904
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowversion1
2nd rowversion1
3rd rowversion2
4th rowversion1
5th rowversion2

Common Values

ValueCountFrequency (%)
version2 257284
50.1%
version1 256579
49.9%

Length

2022-12-29T13:31:26.393204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:31:26.785385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
version2 257284
50.1%
version1 256579
49.9%

Most occurring characters

ValueCountFrequency (%)
v 513863
12.5%
e 513863
12.5%
r 513863
12.5%
s 513863
12.5%
i 513863
12.5%
o 513863
12.5%
n 513863
12.5%
2 257284
6.3%
1 256579
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3597041
87.5%
Decimal Number 513863
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 513863
14.3%
e 513863
14.3%
r 513863
14.3%
s 513863
14.3%
i 513863
14.3%
o 513863
14.3%
n 513863
14.3%
Decimal Number
ValueCountFrequency (%)
2 257284
50.1%
1 256579
49.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3597041
87.5%
Common 513863
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 513863
14.3%
e 513863
14.3%
r 513863
14.3%
s 513863
14.3%
i 513863
14.3%
o 513863
14.3%
n 513863
14.3%
Common
ValueCountFrequency (%)
2 257284
50.1%
1 256579
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4110904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 513863
12.5%
e 513863
12.5%
r 513863
12.5%
s 513863
12.5%
i 513863
12.5%
o 513863
12.5%
n 513863
12.5%
2 257284
6.3%
1 256579
6.2%

success
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0
488312 
1
 
25551

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters513863
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 488312
95.0%
1 25551
 
5.0%

Length

2022-12-29T13:31:27.077533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:31:27.469899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 488312
95.0%
1 25551
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 488312
95.0%
1 25551
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 513863
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 488312
95.0%
1 25551
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 513863
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 488312
95.0%
1 25551
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 488312
95.0%
1 25551
 
5.0%

zipcode
Categorical

Distinct15038
Distinct (%)2.9%
Missing767
Missing (%)0.1%
Memory size3.9 MiB
30080
 
17902
97229
 
10764
30339
 
6014
7205
 
4921
94043
 
4178
Other values (15033)
469317 

Length

Max length9
Median length5
Mean length4.9382689
Min length3

Characters and Unicode

Total characters2533806
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2255 ?
Unique (%)0.4%

Sample

1st row30080
2nd row32081
3rd row91124
4th row60614
5th row76309

Common Values

ValueCountFrequency (%)
30080 17902
 
3.5%
97229 10764
 
2.1%
30339 6014
 
1.2%
7205 4921
 
1.0%
94043 4178
 
0.8%
77035 3445
 
0.7%
7014 2524
 
0.5%
80202 2210
 
0.4%
60602 1878
 
0.4%
10011 1703
 
0.3%
Other values (15028) 457557
89.0%

Length

2022-12-29T13:31:27.754879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30080 17902
 
3.5%
97229 10764
 
2.1%
30339 6014
 
1.2%
7205 4921
 
1.0%
94043 4178
 
0.8%
77035 3445
 
0.7%
7014 2524
 
0.5%
80202 2210
 
0.4%
60602 1878
 
0.4%
0a1 1846
 
0.4%
Other values (15111) 460636
89.2%

Most occurring characters

ValueCountFrequency (%)
0 405509
16.0%
3 308968
12.2%
2 291541
11.5%
1 287650
11.4%
7 235300
9.3%
9 213443
8.4%
8 208757
8.2%
4 200645
7.9%
5 194919
7.7%
6 172744
6.8%
Other values (27) 14330
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2519476
99.4%
Lowercase Letter 10725
 
0.4%
Space Separator 3222
 
0.1%
Dash Punctuation 383
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3115
29.0%
m 989
 
9.2%
l 787
 
7.3%
v 602
 
5.6%
n 601
 
5.6%
h 485
 
4.5%
s 462
 
4.3%
e 452
 
4.2%
g 418
 
3.9%
c 401
 
3.7%
Other values (15) 2413
22.5%
Decimal Number
ValueCountFrequency (%)
0 405509
16.1%
3 308968
12.3%
2 291541
11.6%
1 287650
11.4%
7 235300
9.3%
9 213443
8.5%
8 208757
8.3%
4 200645
8.0%
5 194919
7.7%
6 172744
6.9%
Space Separator
ValueCountFrequency (%)
3222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 383
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2523081
99.6%
Latin 10725
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3115
29.0%
m 989
 
9.2%
l 787
 
7.3%
v 602
 
5.6%
n 601
 
5.6%
h 485
 
4.5%
s 462
 
4.3%
e 452
 
4.2%
g 418
 
3.9%
c 401
 
3.7%
Other values (15) 2413
22.5%
Common
ValueCountFrequency (%)
0 405509
16.1%
3 308968
12.2%
2 291541
11.6%
1 287650
11.4%
7 235300
9.3%
9 213443
8.5%
8 208757
8.3%
4 200645
8.0%
5 194919
7.7%
6 172744
6.8%
Other values (2) 3605
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2533806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 405509
16.0%
3 308968
12.2%
2 291541
11.5%
1 287650
11.4%
7 235300
9.3%
9 213443
8.4%
8 208757
8.2%
4 200645
7.9%
5 194919
7.7%
6 172744
6.8%
Other values (27) 14330
 
0.6%

pro
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0
421771 
1
92092 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters513863
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 421771
82.1%
1 92092
 
17.9%

Length

2022-12-29T13:31:28.073622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:31:28.367345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 421771
82.1%
1 92092
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 421771
82.1%
1 92092
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 513863
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 421771
82.1%
1 92092
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 513863
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 421771
82.1%
1 92092
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 421771
82.1%
1 92092
 
17.9%

repeat_visit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1
416487 
0
97376 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters513863
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 416487
81.1%
0 97376
 
18.9%

Length

2022-12-29T13:31:28.678413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:31:29.058259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 416487
81.1%
0 97376
 
18.9%

Most occurring characters

ValueCountFrequency (%)
1 416487
81.1%
0 97376
 
18.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 513863
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 416487
81.1%
0 97376
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
Common 513863
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 416487
81.1%
0 97376
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 416487
81.1%
0 97376
 
18.9%

days_since_last_visit
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing107835
Missing (%)21.0%
Memory size3.9 MiB
less than 1 day
230358 
less than 7 days
96195 
first visit
56522 
more than 7 days
 
21412
more than 30 days
 
1455

Length

Max length17
Median length15
Mean length14.740203
Min length11

Characters and Unicode

Total characters5984935
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowless than 7 days
2nd rowless than 7 days
3rd rowless than 1 day
4th rowless than 7 days
5th rowmore than 7 days

Common Values

ValueCountFrequency (%)
less than 1 day 230358
44.8%
less than 7 days 96195
18.7%
first visit 56522
 
11.0%
more than 7 days 21412
 
4.2%
more than 30 days 1455
 
0.3%
more than a year 86
 
< 0.1%
(Missing) 107835
21.0%

Length

2022-12-29T13:31:29.370183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:31:29.763652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
than 349506
23.1%
less 326553
21.6%
1 230358
15.2%
day 230358
15.2%
days 119062
 
7.9%
7 117607
 
7.8%
first 56522
 
3.7%
visit 56522
 
3.7%
more 22953
 
1.5%
30 1455
 
0.1%
Other values (2) 172
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1105040
18.5%
s 885212
14.8%
a 699098
11.7%
t 462550
7.7%
e 349592
 
5.8%
y 349506
 
5.8%
h 349506
 
5.8%
n 349506
 
5.8%
d 349420
 
5.8%
l 326553
 
5.5%
Other values (10) 758952
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4529020
75.7%
Space Separator 1105040
 
18.5%
Decimal Number 350875
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 885212
19.5%
a 699098
15.4%
t 462550
10.2%
e 349592
 
7.7%
y 349506
 
7.7%
h 349506
 
7.7%
n 349506
 
7.7%
d 349420
 
7.7%
l 326553
 
7.2%
i 169566
 
3.7%
Other values (5) 238511
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 230358
65.7%
7 117607
33.5%
3 1455
 
0.4%
0 1455
 
0.4%
Space Separator
ValueCountFrequency (%)
1105040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4529020
75.7%
Common 1455915
 
24.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 885212
19.5%
a 699098
15.4%
t 462550
10.2%
e 349592
 
7.7%
y 349506
 
7.7%
h 349506
 
7.7%
n 349506
 
7.7%
d 349420
 
7.7%
l 326553
 
7.2%
i 169566
 
3.7%
Other values (5) 238511
 
5.3%
Common
ValueCountFrequency (%)
1105040
75.9%
1 230358
 
15.8%
7 117607
 
8.1%
3 1455
 
0.1%
0 1455
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5984935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1105040
18.5%
s 885212
14.8%
a 699098
11.7%
t 462550
7.7%
e 349592
 
5.8%
y 349506
 
5.8%
h 349506
 
5.8%
n 349506
 
5.8%
d 349420
 
5.8%
l 326553
 
5.5%
Other values (10) 758952
12.7%

new_movers
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0
347126 
1
166737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters513863
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 347126
67.6%
1 166737
32.4%

Length

2022-12-29T13:31:30.152267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:31:30.475823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 347126
67.6%
1 166737
32.4%

Most occurring characters

ValueCountFrequency (%)
0 347126
67.6%
1 166737
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 513863
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 347126
67.6%
1 166737
32.4%

Most occurring scripts

ValueCountFrequency (%)
Common 513863
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 347126
67.6%
1 166737
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 347126
67.6%
1 166737
32.4%

year_home_built
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct109
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean1980.672
Minimum0
Maximum2017
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:30.770058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1957
Q11973
median1984
Q31989
95-th percentile1998
Maximum2017
Range2017
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.098487
Coefficient of variation (CV)0.0066131532
Kurtosis1041.7595
Mean1980.672
Median Absolute Deviation (MAD)8
Skewness-7.5693038
Sum9.9410126 × 108
Variance171.57037
MonotonicityNot monotonic
2022-12-29T13:31:31.152257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1987 34895
 
6.8%
1986 25640
 
5.0%
1989 25215
 
4.9%
1988 17810
 
3.5%
1978 17477
 
3.4%
1976 16551
 
3.2%
1984 16078
 
3.1%
1983 15573
 
3.0%
1985 15233
 
3.0%
1979 13584
 
2.6%
Other values (99) 303845
59.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1900 13
< 0.1%
1902 4
 
< 0.1%
1904 6
< 0.1%
1905 1
 
< 0.1%
1908 1
 
< 0.1%
1910 8
< 0.1%
1911 1
 
< 0.1%
1914 12
< 0.1%
1915 1
 
< 0.1%
ValueCountFrequency (%)
2017 6
 
< 0.1%
2016 3
 
< 0.1%
2015 6
 
< 0.1%
2014 4
 
< 0.1%
2013 5
 
< 0.1%
2012 26
 
< 0.1%
2011 8
 
< 0.1%
2010 20
 
< 0.1%
2009 3
 
< 0.1%
2008 72
< 0.1%

home_market_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct886
Distinct (%)0.2%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean343.19592
Minimum4
Maximum2628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:31.603408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile171
Q1239
median310
Q3413
95-th percentile630
Maximum2628
Range2624
Interquartile range (IQR)174

Descriptive statistics

Standard deviation150.48762
Coefficient of variation (CV)0.43848896
Kurtosis4.3253086
Mean343.19592
Median Absolute Deviation (MAD)79
Skewness1.5776755
Sum1.7225037 × 108
Variance22646.524
MonotonicityNot monotonic
2022-12-29T13:31:32.036812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320 18673
 
3.6%
555 10964
 
2.1%
266 7954
 
1.5%
293 5622
 
1.1%
235 5480
 
1.1%
520 4407
 
0.9%
442 4183
 
0.8%
416 3559
 
0.7%
247 3440
 
0.7%
360 3282
 
0.6%
Other values (876) 434337
84.5%
(Missing) 11962
 
2.3%
ValueCountFrequency (%)
4 6
 
< 0.1%
10 6
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 66
< 0.1%
17 6
 
< 0.1%
19 1
 
< 0.1%
20 3
 
< 0.1%
24 5
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
2628 1
 
< 0.1%
2496 1
 
< 0.1%
1991 4
 
< 0.1%
1842 3
 
< 0.1%
1790 3
 
< 0.1%
1650 15
< 0.1%
1563 16
< 0.1%
1493 28
< 0.1%
1492 5
 
< 0.1%
1483 21
< 0.1%

length_of_residence
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean11.004557
Minimum0.25
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:32.362793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile8
Q18
median13
Q313
95-th percentile13
Maximum23
Range22.75
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9633743
Coefficient of variation (CV)0.26928612
Kurtosis0.43215711
Mean11.004557
Median Absolute Deviation (MAD)0
Skewness0.29332319
Sum5523198
Variance8.7815874
MonotonicityNot monotonic
2022-12-29T13:31:32.890568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13 268995
52.3%
8 209301
40.7%
18 14750
 
2.9%
4 5522
 
1.1%
23 2768
 
0.5%
1.5 303
 
0.1%
0.75 166
 
< 0.1%
0.25 96
 
< 0.1%
(Missing) 11962
 
2.3%
ValueCountFrequency (%)
0.25 96
 
< 0.1%
0.75 166
 
< 0.1%
1.5 303
 
0.1%
4 5522
 
1.1%
8 209301
40.7%
13 268995
52.3%
18 14750
 
2.9%
23 2768
 
0.5%
ValueCountFrequency (%)
23 2768
 
0.5%
18 14750
 
2.9%
13 268995
52.3%
8 209301
40.7%
4 5522
 
1.1%
1.5 303
 
0.1%
0.75 166
 
< 0.1%
0.25 96
 
< 0.1%

net_worth
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean288908.64
Minimum12500
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:33.159048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12500
5-th percentile150000
Q1150000
median250000
Q3250000
95-th percentile750000
Maximum1000000
Range987500
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation165392.37
Coefficient of variation (CV)0.5724729
Kurtosis3.2696796
Mean288908.64
Median Absolute Deviation (MAD)0
Skewness1.7509761
Sum1.4500354 × 1011
Variance2.7354636 × 1010
MonotonicityNot monotonic
2022-12-29T13:31:33.497854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
250000 270236
52.6%
150000 104374
 
20.3%
500000 77763
 
15.1%
750000 21935
 
4.3%
100000 17394
 
3.4%
1000000 4411
 
0.9%
75000 2930
 
0.6%
50000 1044
 
0.2%
12500 961
 
0.2%
25000 853
 
0.2%
(Missing) 11962
 
2.3%
ValueCountFrequency (%)
12500 961
 
0.2%
25000 853
 
0.2%
50000 1044
 
0.2%
75000 2930
 
0.6%
100000 17394
 
3.4%
150000 104374
 
20.3%
250000 270236
52.6%
500000 77763
 
15.1%
750000 21935
 
4.3%
1000000 4411
 
0.9%
ValueCountFrequency (%)
1000000 4411
 
0.9%
750000 21935
 
4.3%
500000 77763
 
15.1%
250000 270236
52.6%
150000 104374
 
20.3%
100000 17394
 
3.4%
75000 2930
 
0.6%
50000 1044
 
0.2%
25000 853
 
0.2%
12500 961
 
0.2%

income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean100913.02
Minimum10000
Maximum250000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:33.868106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile75000
Q175000
median100000
Q3125000
95-th percentile150000
Maximum250000
Range240000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation28175.24
Coefficient of variation (CV)0.27920323
Kurtosis1.5045767
Mean100913.02
Median Absolute Deviation (MAD)25000
Skewness0.80978464
Sum5.0648345 × 1010
Variance7.9384417 × 108
MonotonicityNot monotonic
2022-12-29T13:31:34.162492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100000 179388
34.9%
75000 149998
29.2%
125000 113926
22.2%
50000 19673
 
3.8%
150000 17741
 
3.5%
175000 15706
 
3.1%
200000 3202
 
0.6%
40000 871
 
0.2%
250000 519
 
0.1%
30000 461
 
0.1%
Other values (3) 416
 
0.1%
(Missing) 11962
 
2.3%
ValueCountFrequency (%)
10000 114
 
< 0.1%
15000 81
 
< 0.1%
20000 221
 
< 0.1%
30000 461
 
0.1%
40000 871
 
0.2%
50000 19673
 
3.8%
75000 149998
29.2%
100000 179388
34.9%
125000 113926
22.2%
150000 17741
 
3.5%
ValueCountFrequency (%)
250000 519
 
0.1%
200000 3202
 
0.6%
175000 15706
 
3.1%
150000 17741
 
3.5%
125000 113926
22.2%
100000 179388
34.9%
75000 149998
29.2%
50000 19673
 
3.8%
40000 871
 
0.2%
30000 461
 
0.1%

mkt_organic_product_purchasers_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean38.220549
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:34.609530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q129
median36
Q346
95-th percentile64
Maximum99
Range98
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.052736
Coefficient of variation (CV)0.34151096
Kurtosis0.63336244
Mean38.220549
Median Absolute Deviation (MAD)7
Skewness0.82829228
Sum19182932
Variance170.37393
MonotonicityNot monotonic
2022-12-29T13:31:35.022386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 31227
 
6.1%
34 23394
 
4.6%
32 22708
 
4.4%
23 18890
 
3.7%
37 18398
 
3.6%
36 17963
 
3.5%
33 17860
 
3.5%
35 15786
 
3.1%
38 15731
 
3.1%
31 15148
 
2.9%
Other values (89) 304796
59.3%
ValueCountFrequency (%)
1 59
< 0.1%
2 30
 
< 0.1%
3 45
 
< 0.1%
4 77
< 0.1%
5 51
< 0.1%
6 37
 
< 0.1%
7 50
< 0.1%
8 16
 
< 0.1%
9 115
< 0.1%
10 76
< 0.1%
ValueCountFrequency (%)
99 15
 
< 0.1%
98 18
 
< 0.1%
97 11
 
< 0.1%
96 47
 
< 0.1%
95 68
< 0.1%
94 40
 
< 0.1%
93 24
 
< 0.1%
92 37
 
< 0.1%
91 123
< 0.1%
90 60
< 0.1%

mkt_trend_env_focused_hh_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean31.381515
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:35.396799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q121
median30
Q340
95-th percentile57
Maximum99
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.952084
Coefficient of variation (CV)0.44459561
Kurtosis0.33974362
Mean31.381515
Median Absolute Deviation (MAD)9
Skewness0.65046944
Sum15750414
Variance194.66065
MonotonicityNot monotonic
2022-12-29T13:31:35.748526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 31830
 
6.2%
32 22194
 
4.3%
30 17435
 
3.4%
11 16407
 
3.2%
20 16075
 
3.1%
22 13589
 
2.6%
28 13392
 
2.6%
27 13374
 
2.6%
29 12974
 
2.5%
26 12604
 
2.5%
Other values (89) 332027
64.6%
ValueCountFrequency (%)
1 29
 
< 0.1%
2 40
 
< 0.1%
3 59
 
< 0.1%
4 190
 
< 0.1%
5 634
 
0.1%
6 1151
 
0.2%
7 1604
 
0.3%
8 5232
1.0%
9 2297
0.4%
10 5316
1.0%
ValueCountFrequency (%)
99 4
 
< 0.1%
98 41
< 0.1%
97 14
 
< 0.1%
96 20
 
< 0.1%
95 25
< 0.1%
94 25
< 0.1%
93 47
< 0.1%
92 41
< 0.1%
91 33
< 0.1%
90 54
< 0.1%

high_end_shoppers_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean31.23937
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:36.070632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q125
median30
Q337
95-th percentile49
Maximum99
Range98
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.195232
Coefficient of variation (CV)0.32635843
Kurtosis1.7434001
Mean31.23937
Median Absolute Deviation (MAD)6
Skewness0.86842298
Sum15679071
Variance103.94275
MonotonicityNot monotonic
2022-12-29T13:31:36.425642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 39099
 
7.6%
26 31695
 
6.2%
33 23228
 
4.5%
23 20568
 
4.0%
28 20438
 
4.0%
37 17684
 
3.4%
36 17643
 
3.4%
29 16916
 
3.3%
31 16480
 
3.2%
27 16301
 
3.2%
Other values (89) 281849
54.8%
ValueCountFrequency (%)
1 29
 
< 0.1%
2 61
 
< 0.1%
3 40
 
< 0.1%
4 47
 
< 0.1%
5 75
 
< 0.1%
6 90
 
< 0.1%
7 73
 
< 0.1%
8 98
 
< 0.1%
9 365
0.1%
10 766
0.1%
ValueCountFrequency (%)
99 13
 
< 0.1%
98 8
 
< 0.1%
97 14
 
< 0.1%
96 22
< 0.1%
95 34
< 0.1%
94 11
 
< 0.1%
93 7
 
< 0.1%
92 19
< 0.1%
91 36
< 0.1%
90 22
< 0.1%

do_it_yourselfer_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean46.192526
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:36.850592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q138
median47
Q355
95-th percentile67
Maximum99
Range98
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.655106
Coefficient of variation (CV)0.27396437
Kurtosis0.1197245
Mean46.192526
Median Absolute Deviation (MAD)8
Skewness-0.23593066
Sum23184075
Variance160.15172
MonotonicityNot monotonic
2022-12-29T13:31:37.255853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 28010
 
5.5%
47 24948
 
4.9%
54 20194
 
3.9%
45 17668
 
3.4%
50 17449
 
3.4%
52 16650
 
3.2%
42 15363
 
3.0%
49 14986
 
2.9%
48 13949
 
2.7%
44 13896
 
2.7%
Other values (88) 318788
62.0%
ValueCountFrequency (%)
1 119
 
< 0.1%
2 75
 
< 0.1%
3 132
 
< 0.1%
4 165
 
< 0.1%
5 198
< 0.1%
6 223
< 0.1%
7 451
0.1%
8 313
0.1%
9 319
0.1%
10 486
0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
98 16
< 0.1%
97 10
< 0.1%
96 1
 
< 0.1%
95 14
< 0.1%
93 10
< 0.1%
92 7
< 0.1%
91 15
< 0.1%
90 1
 
< 0.1%
89 5
 
< 0.1%
Distinct98
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean30.515315
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:37.633827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q125
median29
Q335
95-th percentile46
Maximum99
Range98
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9727219
Coefficient of variation (CV)0.29403996
Kurtosis3.2071592
Mean30.515315
Median Absolute Deviation (MAD)5
Skewness1.0847744
Sum15315667
Variance80.509738
MonotonicityNot monotonic
2022-12-29T13:31:38.058500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 41698
 
8.1%
29 31195
 
6.1%
30 24892
 
4.8%
24 24452
 
4.8%
32 24225
 
4.7%
26 23569
 
4.6%
27 22352
 
4.3%
25 20939
 
4.1%
23 19484
 
3.8%
33 18411
 
3.6%
Other values (88) 250684
48.8%
ValueCountFrequency (%)
1 60
 
< 0.1%
2 27
 
< 0.1%
3 31
 
< 0.1%
4 104
 
< 0.1%
5 99
 
< 0.1%
6 464
0.1%
7 100
 
< 0.1%
8 110
 
< 0.1%
9 228
< 0.1%
10 206
< 0.1%
ValueCountFrequency (%)
99 15
< 0.1%
97 4
 
< 0.1%
96 14
< 0.1%
95 14
< 0.1%
94 8
 
< 0.1%
93 26
< 0.1%
92 27
< 0.1%
91 24
< 0.1%
90 14
< 0.1%
89 18
< 0.1%

mkt_green_product_purchasers_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)< 0.1%
Missing11962
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean44.994419
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2022-12-29T13:31:38.486254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q138
median46
Q351
95-th percentile62
Maximum99
Range98
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.438021
Coefficient of variation (CV)0.23198479
Kurtosis0.52123524
Mean44.994419
Median Absolute Deviation (MAD)7
Skewness0.19278834
Sum22582744
Variance108.95228
MonotonicityNot monotonic
2022-12-29T13:31:38.958758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 32741
 
6.4%
47 28743
 
5.6%
46 28697
 
5.6%
51 27959
 
5.4%
35 19953
 
3.9%
40 19574
 
3.8%
49 18505
 
3.6%
42 17701
 
3.4%
44 17633
 
3.4%
50 16826
 
3.3%
Other values (89) 273569
53.2%
ValueCountFrequency (%)
1 27
 
< 0.1%
2 5
 
< 0.1%
3 25
 
< 0.1%
4 6
 
< 0.1%
5 26
 
< 0.1%
6 62
< 0.1%
7 33
 
< 0.1%
8 100
< 0.1%
9 44
< 0.1%
10 40
 
< 0.1%
ValueCountFrequency (%)
99 6
 
< 0.1%
98 15
 
< 0.1%
97 54
< 0.1%
96 36
< 0.1%
95 36
< 0.1%
94 39
< 0.1%
93 46
< 0.1%
92 49
< 0.1%
91 61
< 0.1%
90 51
< 0.1%

Interactions

2022-12-29T13:31:10.402330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:06.910854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:11.967766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:16.780023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:22.141930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:27.608404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:33.272805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:39.978527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:46.754916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:53.502979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:59.546086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:05.063126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:10.824500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:07.362808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:12.423688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:17.161664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:22.640484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:28.102780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:33.748147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:40.479191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:47.370848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:53.930435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:59.959038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:05.492220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:11.266158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:07.796939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:12.846431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:17.608136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:23.194459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:28.610509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:34.286965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:41.101619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:47.953498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:54.433239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:00.462468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:05.963247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:11.676891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:08.227538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:13.237362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:18.057030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:23.613306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:29.091500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:34.782928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:41.786178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:48.834227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:55.008559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:00.929857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:06.376090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:12.143567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:08.639505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:13.623065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:18.455540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:24.040912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:29.536266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:35.234932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:42.656178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:49.721414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:55.610525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:01.340852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:06.789704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:12.588451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:09.053833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:14.031378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:18.854442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:24.476390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:30.048157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:35.784576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:43.333437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:50.189029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:56.148823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:01.791574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:07.258739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:13.046819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:09.446003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:14.405982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:19.287393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:24.998262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:30.539521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:36.395710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:43.829689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:50.815340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:56.798342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:02.403051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:07.675574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:13.516652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:09.861701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:14.811187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:19.682681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:25.413682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:31.004762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:36.915293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:44.306595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:51.304936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:57.293252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:02.824261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:08.141726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:14.149858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:10.240032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:15.210658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:20.132072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:25.820318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:31.434055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:37.359486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:44.780024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:51.780727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:57.772674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:03.255403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:08.582666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:14.524929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:10.671232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:15.599832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:20.562930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:26.262518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:31.858811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:37.832816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:45.286694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:52.202364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:58.219472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:03.697968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:09.052410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:15.101447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:11.109226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:15.998455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:21.044017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:26.701781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:32.271817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:38.746689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:45.800129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:52.614806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:58.681217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:04.108004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:09.520580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:15.629643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:11.547851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:16.384673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:21.699656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:27.172338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:32.767289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:39.398240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:46.247748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:53.058689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:30:59.098596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:04.649651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-29T13:31:09.916688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-29T13:31:39.320763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
visit_idyear_home_builthome_market_valuelength_of_residencenet_worthincomemkt_organic_product_purchasers_valuemkt_trend_env_focused_hh_valuehigh_end_shoppers_valuedo_it_yourselfer_valuemontrd_home_security_sys_own_valuemkt_green_product_purchasers_valueexperiencesuccessprorepeat_visitdays_since_last_visitnew_movers
visit_id1.000-0.0240.0020.0350.0060.0080.0240.0070.020-0.0290.0130.0180.0140.0190.0410.0420.0440.006
year_home_built-0.0241.000-0.176-0.323-0.123-0.0370.1420.3670.185-0.177-0.4150.0930.0000.0000.0000.0001.0000.000
home_market_value0.002-0.1761.000-0.1240.7080.763-0.592-0.737-0.6030.562-0.053-0.4680.0130.0170.0390.0560.0770.008
length_of_residence0.035-0.323-0.1241.0000.056-0.0710.1640.1000.071-0.1190.0080.2240.0090.0140.0460.0290.0340.006
net_worth0.006-0.1230.7080.0561.0000.760-0.321-0.485-0.5220.424-0.170-0.1780.0130.0170.0190.0570.0720.002
income0.008-0.0370.763-0.0710.7601.000-0.341-0.474-0.5690.472-0.207-0.1790.0140.0160.0280.0710.0880.001
mkt_organic_product_purchasers_value0.0240.142-0.5920.164-0.321-0.3411.0000.6940.430-0.5100.1010.6210.0090.0240.0510.0410.1180.010
mkt_trend_env_focused_hh_value0.0070.367-0.7370.100-0.485-0.4740.6941.0000.558-0.622-0.1000.6060.0100.0170.0580.0550.1620.004
high_end_shoppers_value0.0200.185-0.6030.071-0.522-0.5690.4300.5581.000-0.7230.2070.2450.0100.0220.0480.0230.0510.004
do_it_yourselfer_value-0.029-0.1770.562-0.1190.4240.472-0.510-0.622-0.7231.000-0.248-0.3730.0190.0250.0600.0310.0800.005
montrd_home_security_sys_own_value0.013-0.415-0.0530.008-0.170-0.2070.101-0.1000.207-0.2481.000-0.0620.0160.0150.0400.0300.0690.005
mkt_green_product_purchasers_value0.0180.093-0.4680.224-0.178-0.1790.6210.6060.245-0.373-0.0621.0000.0060.0230.0520.0390.0750.003
experience0.0140.0000.0130.0090.0130.0140.0090.0100.0100.0190.0160.0061.0000.0000.0000.0010.0100.428
success0.0190.0000.0170.0140.0170.0160.0240.0170.0220.0250.0150.0230.0001.0000.0480.0350.0770.006
pro0.0410.0000.0390.0460.0190.0280.0510.0580.0480.0600.0400.0520.0000.0481.0000.0840.1830.000
repeat_visit0.0420.0000.0560.0290.0570.0710.0410.0550.0230.0310.0300.0390.0010.0350.0841.0000.2260.000
days_since_last_visit0.0441.0000.0770.0340.0720.0880.1180.1620.0510.0800.0690.0750.0100.0770.1830.2261.0000.007
new_movers0.0060.0000.0080.0060.0020.0010.0100.0040.0040.0050.0050.0030.4280.0060.0000.0000.0071.000

Missing values

2022-12-29T13:31:16.721648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-29T13:31:19.502033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-29T13:31:23.445629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

visit_iddate_timeexperiencesuccesszipcodeprorepeat_visitdays_since_last_visitnew_moversyear_home_builthome_market_valuelength_of_residencenet_worthincomemkt_organic_product_purchasers_valuemkt_trend_env_focused_hh_valuehigh_end_shoppers_valuedo_it_yourselfer_valuemontrd_home_security_sys_own_valuemkt_green_product_purchasers_value
0141675/31/19 10:36version103008000NaN01987.0320.08.0250000.0100000.029.025.025.055.028.039.0
11955815/31/19 10:37version103208110NaN02006.0366.04.0250000.0125000.040.028.023.060.018.046.0
24511425/31/19 10:37version209112400NaN01988.0504.013.0500000.0125000.021.012.016.076.032.031.0
32784965/31/19 10:37version106061410NaN01968.0433.08.0500000.0125000.037.010.012.080.040.037.0
43167575/31/19 10:37version207630901less than 7 days01976.0208.08.0250000.0100000.052.048.024.035.024.062.0
54862525/31/19 10:37version216540100NaN01987.0176.013.0150000.075000.069.025.055.020.040.038.0
61219965/31/19 10:37version206071200NaN01957.0406.013.0250000.0100000.029.024.018.063.037.053.0
73630585/31/19 10:37version208711211less than 7 days01974.0227.013.0250000.075000.039.022.042.038.033.039.0
82464365/31/19 10:37version107211601less than 1 day11973.0215.013.0150000.075000.047.056.030.041.017.061.0
91087705/31/19 10:38version20188600NaN01979.0497.013.0500000.0150000.032.013.020.046.038.051.0
visit_iddate_timeexperiencesuccesszipcodeprorepeat_visitdays_since_last_visitnew_moversyear_home_builthome_market_valuelength_of_residencenet_worthincomemkt_organic_product_purchasers_valuemkt_trend_env_focused_hh_valuehigh_end_shoppers_valuedo_it_yourselfer_valuemontrd_home_security_sys_own_valuemkt_green_product_purchasers_value
513853133466/30/19 23:58version106045311less than 7 days11961.0228.018.0250000.075000.047.049.035.038.036.062.0
5138544714906/30/19 23:58version203377801less than 1 day01973.0254.013.0250000.075000.044.031.040.038.029.050.0
5138551088076/30/19 23:58version10720501first visit01958.0293.013.0250000.075000.019.017.028.054.024.055.0
513856698886/30/19 23:58version209600101less than 7 days01985.0246.013.0150000.075000.037.030.042.032.035.036.0
5138571086736/30/19 23:58version209722901first visit01989.0555.08.0500000.0125000.023.011.026.047.029.035.0
5138583829216/30/19 23:59version209836211less than 1 day01978.0267.013.0250000.0100000.042.031.052.028.042.037.0
5138591863566/30/19 23:59version107271201less than 1 day02001.0260.08.0250000.0100000.045.055.029.039.024.046.0
5138603554316/30/19 23:59version106314601less than 1 day11970.0237.013.0250000.0100000.046.032.033.042.033.049.0
5138614804246/30/19 23:59version107052601NaN12000.0170.018.0100000.075000.066.052.035.036.027.067.0
5138624544256/30/19 23:59version109453801less than 1 day01966.0507.013.0250000.0125000.033.021.032.053.032.028.0